import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from cryptography.hazmat.backends import default_backend
import hashlib
import time
import random

# --- Module 1: Isomorphic Noise Generation ---
class IsomorphicNoiseGenerator:
    """
    Generates isomorphic Gaussian noise using AES-256 encryption
    with shared seeds for reproducible noise patterns across clients.
    """
    def __init__(self, seed, dimensions, sigma=1.0):
        """
        Initialize noise generator with shared seed.
        :param seed: Shared secret seed (bytes)
        :param dimensions: Dimension of noise vector
        :param sigma: Standard deviation of Gaussian noise
        """
        self.seed = seed
        self.dim = dimensions
        self.sigma = sigma
        self.aes_key = self._derive_aes_key(seed)

    def _derive_aes_key(self, seed):
        """Derive 256-bit AES key from seed using SHA-256"""
        return hashlib.sha256(seed).digest()

    def generate_noise(self, timestamp):
        """
        Generate isomorphic noise for given timestamp
        :param timestamp: Training round identifier
        :return: Noise tensor of shape (self.dim)
        """
        # AES encryption with timestamp
        nonce = timestamp.to_bytes(16, 'big')
        cipher = Cipher(algorithms.AES(self.aes_key), modes.CTR(nonce), backend=default_backend())
        encryptor = cipher.encryptor()
        
        # Generate random bytes and map to Gaussian distribution
        random_bytes = encryptor.update(b'\x00' * self.dim * 4) + encryptor.finalize()
        random_floats = np.frombuffer(random_bytes, dtype=np.float32)
        gaussian_noise = random_floats * self.sigma
        return torch.tensor(gaussian_noise[:self.dim])

# --- Module 2: Zero-Loss Recovery Mechanism ---
class NoiseRevocationEngine:
    """
    Implements noise cancellation in aggregated global model
    using isomorphic noise reconstruction
    """
    @staticmethod
    def apply_noise(model_params, noise_vector):
        """Inject isomorphic noise into model parameters"""
        return [param + noise for param in model_params]

    @staticmethod
    def remove_noise(noisy_params, noise_vector):
        """Remove isomorphic noise from model parameters"""
        return [param - noise for param in noisy_params]

# --- Module 3: Quantum-Auditable Protocol ---
class QuantumAuditSystem:
    """
    Simulates quantum-resistant verification using zk-STARKs
    and quantum random walk tags (classical simulation)
    """
    def __init__(self):
        self.entropy_factor = 1.2  # Initial entropy expansion coefficient

    def generate_quantum_tag(self, client_id, timestamp):
        """
        Simulate quantum random walk tag generation
        :param client_id: Unique client identifier
        :param timestamp: Training round
        :return: Simulated quantum tag (hash)
        """
        # In real implementation: Quantum random walk evolution
        # Simulation: Cryptographic hash of entangled parameters
        base = f"{client_id}-{timestamp}-{self.entropy_factor}".encode()
        return hashlib.sha3_256(base).digest()

    def verify_consistency(self, tags):
        """
        Verify consistency of quantum tags across clients
        :param tags: List of tags from all clients
        :return: True if all tags match, False otherwise
        """
        return all(tag == tags[0] for tag in tags)

    def update_entropy(self, round_num, total_rounds):
        """Dynamically increase entropy over training rounds"""
        self.entropy_factor = 1.2 + 0.1 * np.tanh(round_num / total_rounds)

# --- Module 4: Dynamic Coordinated Optimizer ---
class DynamicNoiseOptimizer:
    """
    Dynamically adjusts noise parameters based on system state
    using federated clustering and reinforcement learning (simplified)
    """
    def __init__(self, min_sigma=0.5, max_sigma=2.0):
        self.min_sigma = min_sigma
        self.max_sigma = max_sigma
        self.cluster_threshold = 0.8

    def adapt_noise_parameters(self, gradient_variance, bandwidth, energy_level):
        """
        Adapt noise parameters based on system state
        :param gradient_variance: Measure of data heterogeneity
        :param bandwidth: Current network bandwidth
        :param energy_level: Client device energy status
        :return: Optimized noise sigma value
        """
        # Simplified adaptation logic (RL would be used in production)
        if gradient_variance > 0.5 or energy_level < 0.3:
            return self.min_sigma  # Reduce noise for high heterogeneity/low energy
        elif bandwidth < 50:  # Mbps
            return self.min_sigma  # Reduce noise for low bandwidth
        else:
            return self.max_sigma  # Use stronger noise otherwise

    def update_clustering_threshold(self, round_num, total_rounds):
        """Dynamically adjust federated clustering threshold"""
        self.cluster_threshold = 0.8 + 0.1 * np.tanh(round_num / total_rounds)

# --- Module 5: Differential Privacy Compliance ---
class PrivacyComplianceChecker:
    """
    Validates differential privacy guarantees using Gaussian mechanism
    """
    @staticmethod
    def calculate_epsilon(sigma, delta=1e-5):
        """
        Calculate (ε, δ)-DP guarantee for Gaussian mechanism
        :param sigma: Noise standard deviation
        :param delta: Privacy failure probability
        :return: ε privacy budget
        """
        return np.sqrt(2 * np.log(1.25 / delta)) / sigma

    @staticmethod
    def check_violation(epsilon, threshold=0.35):
        """Check if privacy budget exceeds threshold"""
        return epsilon > threshold

# --- Module 6: Federated Client Implementation ---
class FLClient:
    """Federated learning client with noise revocation capabilities"""
    def __init__(self, client_id, data, model, seed):
        self.client_id = client_id
        self.data_loader = DataLoader(data, batch_size=32, shuffle=True)
        self.model = model
        self.noise_gen = IsomorphicNoiseGenerator(seed, self._count_parameters())
        self.quantum_audit = QuantumAuditSystem()
        self.noise_optimizer = DynamicNoiseOptimizer()

    def _count_parameters(self):
        """Count trainable parameters in model"""
        return sum(p.numel() for p in self.model.parameters() if p.requires_grad)

    def local_train(self, round_num, total_rounds):
        """Perform local training with noise injection"""
        optimizer = optim.SGD(self.model.parameters(), lr=0.01)
        criterion = nn.CrossEntropyLoss()
        
        # Update dynamic parameters
        self.quantum_audit.update_entropy(round_num, total_rounds)
        self.noise_optimizer.update_clustering_threshold(round_num, total_rounds)
        
        # Generate quantum audit tag
        quantum_tag = self.quantum_audit.generate_quantum_tag(self.client_id, round_num)
        
        # Train model locally
        for inputs, labels in self.data_loader:
            optimizer.zero_grad()
            outputs = self.model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
        
        # Generate and apply isomorphic noise
        noise_vector = self.noise_gen.generate_noise(round_num)
        noisy_params = NoiseRevocationEngine.apply_noise(
            list(self.model.parameters()), noise_vector
        )
        
        return noisy_params, quantum_tag

# --- Module 7: Federated Server Implementation ---
class FLServer:
    """Federated learning server with noise revocation"""
    def __init__(self, global_model):
        self.global_model = global_model
        self.quantum_audit = QuantumAuditSystem()

    def aggregate_models(self, client_updates, quantum_tags):
        """
        Aggregate client updates and verify quantum consistency
        :param client_updates: List of (noisy_params, quantum_tag) from clients
        :return: Aggregated global model parameters
        """
        # Verify quantum consistency
        if not self.quantum_audit.verify_consistency(quantum_tags):
            raise SecurityException("Quantum audit consistency check failed")
        
        # Federated averaging
        avg_params = [torch.zeros_like(param) for param in client_updates[0]]
        for params, _ in client_updates:
            for i, param in enumerate(params):
                avg_params[i] += param / len(client_updates)
        
        return avg_params

    def revoke_global_noise(self, global_params, noise_vector):
        """
        Remove global noise from aggregated model
        :param global_params: Noisy global parameters
        :param noise_vector: Isomorphic noise vector
        :return: Clean global parameters
        """
        return NoiseRevocationEngine.remove_noise(global_params, noise_vector)

# --- Security Exception Class ---
class SecurityException(Exception):
    """Exception for security protocol violations"""
    pass

# --- Example Usage ---
if __name__ == "__main__":
    # Initialize shared components
    shared_seed = b'fed_learning_secret_key'
    global_model = SimpleCNN()  # Placeholder model
    server = FLServer(global_model)
    
    # Create simulated clients
    clients = [
        FLClient(i, train_data[i], copy.deepcopy(global_model), shared_seed)
        for i in range(5)
    ]
    
    # Federated training loop
    total_rounds = 10
    for round_num in range(total_rounds):
        client_updates = []
        quantum_tags = []
        
        # Client local training
        for client in clients:
            noisy_params, q_tag = client.local_train(round_num, total_rounds)
            client_updates.append((noisy_params, q_tag))
            quantum_tags.append(q_tag)
        
        # Server aggregation
        aggregated_params = server.aggregate_models(client_updates, quantum_tags)
        
        # Noise revocation (using first client's noise generator)
        noise_vector = clients[0].noise_gen.generate_noise(round_num)
        clean_params = server.revoke_global_noise(aggregated_params, noise_vector)
        
        # Update global model
        with torch.no_grad():
            for param, new_val in zip(global_model.parameters(), clean_params):
                param.copy_(new_val)
        
        # Privacy compliance check
        current_sigma = clients[0].noise_gen.sigma
        epsilon = PrivacyComplianceChecker.calculate_epsilon(current_sigma)
        print(f"Round {round_num}: ε={epsilon:.4f}")
        
        # Dynamic noise adjustment example
        if PrivacyComplianceChecker.check_violation(epsilon):
            print("Privacy violation detected! Adjusting noise parameters")
            for client in clients:
                client.noise_gen.sigma *= 1.2

# --- Placeholder Model and Dataset ---
class SimpleCNN(nn.Module):
    """Example CNN model for image classification"""
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 16, 3)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(16*13*13, 10)
    
    def forward(self, x):
        x = self.pool(torch.relu(self.conv1(x)))
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        return x

class DummyDataset(Dataset):
    """Placeholder dataset for demonstration"""
    def __init__(self, size=1000):
        self.data = torch.randn(size, 3, 28, 28)
        self.labels = torch.randint(0, 10, (size,))
    
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        return self.data[idx], self.labels[idx]

# Initialize simulated data
train_data = [DummyDataset() for _ in range(5)]
